Analyzing Audio Patterns During the 2024 Total Solar Eclipse

Introduction

• Our goal was to look deeper into effects that the total solar eclipse has on wildlife activity.

• Analyzed audio data using 5 common audiological indices

• Used Generalized Additive Models to produce visuals to search for temporal patterns

• This was an amazing and unique opportunity to look into such a rare phenomenon and to use data that was collected by SLU faculty.

• Inspiration for this analysis was taken from:

Gerber, J. E., Howard, D., & Quinn, J. E. (2020). Soundscape shifts during the 2017 total solar eclipse: An application of dispersed automated recording units to study ephemeral acoustic events. Biodiversity, 21(1), 41–47. https://doi.org/10.1080/14888386.2020.1715834

Audio Recorder Placement

Data Collection

• Recorders collected audio from March 30th to April 16th

• Recorders sampled 55 seconds on and 5 seconds off

• 20 audio recorders produced a total of 115,610 audio clips (>1 TB)

Audio Indices

• Bioacoustic Index - Assesses diversity and abundance of biological noise. Gives higher value if there are more different types of sounds in different frequencies (2-8kHz)

• Biophony - Measures the amount of sound within a certain frequency band

• Acoustic Complexity - Focused on expressing the spatial and temporal complexity in sound by capturing the intensity and frequency shifts across an audio file.

• Acoustic Diversity - Generates proportions over set intervals to assess evenness and variety in sound distribution

• Acoustic Evenness - Measure how sound is distributed across frequency bands, assess equality and inequality of the rate that sound is produced from a source

Villanueva-Rivera LJ, Pijanowski BC (2018). soundecology: Soundscape Ecology. R package version 1.3.3, https://CRAN.R-project.org/package=soundecology.

Using R and HPC

• Function to take sound files and return cleaned data frame with information needed for analysis

• Over a terabyte of data

• Due to massive size, we used St. Lawrence’s HPC (High-Performance Computer) to run and store our files incrementally.

• With the HPC, each folder of clips took 10.5 hours to process.

Generalized Additive Modeling

\[ Index \sim spline(hour, by = day) + audio\_recorder+ day\]

• Fit a smoothing function for time, and have it vary by day

• Account for Audio Recorder differences, but treat this as a random effect

• Adjust for overall differences in day

• Numerically, the estimated degrees of freedom provided a comparison of line curvature between the days

Bioacoustic Model Visualization

Biophony Model Visualization

Findings

• Based on our analysis we observed potential patterns in 3 of our indices:

  • Bioacoustic Index

  • Acoustic Evenness

  • Acoustic Diversity

• These indices showed increases or decreases around the time of the eclipse, specifically with variation around the time of totality.

• This suggests that wildlife is affected by the sudden changes in light that occurred during the solar eclipse or by some other eclipse-driven change.